Power Management Estimating Remaining Use Time
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Solution Overview
Problem
Users of electronic devices are uncertain about the remaining charge sufficiency for upcoming usage periods due to the lack of accurate information provided by traditional battery charge status icons, which do not account for specific power requirements based on user behavior and activities.
Innovation Solution
The electronic device learns user behavior and power usage patterns to estimate remaining use time by creating power profiles that include estimated power requirements, historical usage, and user input, allowing for personalized and continuous display of remaining use time, which can be updated periodically or at select times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional battery charge status icons are used to display remaining charge, then the device structure remains simple, but the user cannot determine if the power source has sufficient charge for upcoming usage periods
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and power consumption rates before the user needs to make decisions about battery usage. By continuously monitoring and learning user habits in advance, the system can predict remaining use time and provide warnings before battery depletion occurs, rather than simply displaying static charge levels.
Solution Approach 2:
The system implements a feedback mechanism where the displayed remaining use time information is based on continuous monitoring of actual power consumption against predicted patterns. This feedback loop allows the system to adjust its predictions based on real-time usage data, providing increasingly accurate information to users about when battery depletion will occur.
2Measurement precision
If the system continuously monitors and learns user behavior to provide accurate remaining use time information, then the information accuracy improves, but the processing power and energy consumption increase
Solution Approach 1:
The system applies partial monitoring and processing based on user needs and device state. Rather than continuously analyzing all possible parameters at full intensity, the system selectively monitors relevant behaviors and adjusts the level of analysis based on battery charge levels and user activity patterns, reducing overall computational energy consumption while maintaining accuracy.
Solution Approach 2:
The system dynamically changes monitoring parameters based on device state. When battery charge is high, monitoring intensity can be reduced. When charge drops below thresholds, the system increases monitoring frequency and processing depth to provide more frequent updates about remaining use time, optimizing the balance between accuracy and energy consumption.
3Adaptability or versatility
If the system provides personalized remaining use time information for different users, then the usefulness of the information increases, but the complexity of user profiling and customization increases
Solution Approach 1:
The system automatically creates and updates user profiles through self-service mechanisms. By monitoring usage patterns and automatically inferring user behavior characteristics, the system eliminates the need for manual user setup and profile configuration. Multiple user profiles are generated and maintained automatically based on observed behavior, reducing the complexity of user profile management while providing personalized information.
Data Source
AI summary
A power source is configured to supply power to one or more components of an electronic device. A processing device that is in communication with the power source can be configured to determine an estimated power requirement of the mobile electronic device during a time period, to determine a charge state of the power source, and to produce an indication of the remaining use time of the electronic device based on the estimated power requirement and the charge state of the power source.


